Ambient clinical intelligence

Medical speech-to-text accurate enough to sign off on

In medical speech-to-text, a misheard drug name isn't a typo, it's a liability. Gladia gives your clinical product custom medical vocabulary, accurate doctor–patient attribution, and HIPAA compliance with a BAA available.

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Used by

Clinical professional with HIPAA and compliance certification badges

90–96%

Transcription accuracy

Custom vocabulary keeps drug names, dosages, and clinical terminology accurate.

Speaker diarization

Doctor and patient speech reliably separated, even in noisy multi-speaker encounters.

40+

PII entity types

Strip patient identifiers and keep clinical terms. Consistent markers, so your LLM still knows who said what.

Why teams switch to Gladia

Accurate enough to chart. Compliant enough to ship.

In clinical documentation, the transcription layer decides whether a note gets signed or rewritten.

Clinical vocabulary

One wrong drug name, real consequences

A misheard dosage or diagnosis isn't a quality issue, it's a liability. Add your specialty terms and abbreviations as custom vocabulary, then extract drugs, conditions, and procedures as structured entities.

Custom medical vocabulary
Drug, condition & procedure extraction
No model retraining

Compliance

Cleared by compliance before you write a line of code

US providers without a BAA, or with no EU option, disqualify themselves before the technical evaluation starts. Gladia is HIPAA compliant with a BAA available, and runs EU and US workloads on separate infrastructure.

HIPAA compliant, BAA available
SOC 2 Type 2, ISO 27001 & ISO 27701
GDPR with EU data residency

Speaker attribution

A symptom isn't a finding

Who said it changes the note. A patient reporting chest pain and a clinician observing it belong in different parts of the record. Gladia labels every word by speaker.

Word-level speaker labels
Configurable speaker count
#1 on speaker diarization (DIHARD III)

Clinical encounter · speakers

I'm seeing elevated liver enzymes on the latest panel. Any new medications?

I started taking ibuprofen daily for knee pain, about three weeks ago.

Let's switch to acetaminophen and recheck in two weeks.

Should I stop the ibuprofen right away?

PII redaction

De-identify the transcript, not the medicine

Choose exactly which entities get redacted: names, dates of birth, record numbers. Medications and diagnoses stay in.

40+ redactable entity types
Per-entity control or regulation presets
Consistent markers for LLM pipelines

Clinical workflows

From consult to structured note in one request

Send the recording after the visit and get back the transcript, speaker labels, and your note format in a single call. Bring your own prompt schema for SOAP or specialty templates.

Async transcription + LLM in one request
Your own note schema
Sub-300ms real-time when you need it

Benchmarks

Speaker attribution, tested against the field

Misattribution puts a patient's words in the clinician's mouth. Gladia leads on DIHARD III, the toughest multi-domain speaker diarization benchmark. DER = lower is better. Our benchmark methodology is open-source.

See the full comparison See the full comparison

16.6%

Solaria-1

20.4%

NVIDIA NeMo

23%

pyannoteAI

33.8%

AWS

37.8%

Soniox

39.5%

ElevenLabs

43.9%

AssemblyAI

46.9%

Deepgram

Getting started

From API key to your first clinical transcript in minutes

Get your API key

€50 in free credits, with diarization, custom vocabulary, and named entity recognition included.

Send your audio

Upload consult recordings for async processing, or stream live over WebSocket. The output schema is the same either way, via API, Python and TypeScript SDKs, or the Playground.

Receive structured clinical output

Speaker-labeled transcripts with drugs, conditions, and procedures extracted, and patient identifiers redacted when you need them. Ready for your note-generation pipeline.

Run compliance in parallel

Request your BAA, DPA, and SOC 2 Type 2 and ISO 27001 reports while you test, so the compliance review doesn't hold up go-live.

Clinical intelligence case study

How a healthcare AI startup made medical transcription 120% faster

Challenge

Doctors were losing 60% of their time to non-clinical computer work

A fast-growing startup building an AI co-pilot for physicians set out to cut that to 15%. Its notes, prescriptions, and EHR enrichment all depended on transcribing jargon-heavy consultations accurately and fast enough to generate notes right after the visit, all under HIPAA and GDPR. They tested more than seven providers, Big Tech clouds included, and none balanced accuracy, speed, price, and security.

Trusted in production by teams building clinical AI

Matthias Wickenburg CTO & Co-founder at Attention

We initially attempted to host Whisper AI, which required significant effort to scale. Switching to Gladia brought a welcome change.

Robin Lambert CTO at Livestorm

Amanda Zhu Co-Founder at Recall

Gladia's real-time code-switching has been a real 'wow' factor! Plus, the accuracy of transcription has been excellent.